<p>Efficient path planning for unmanned surface vehicles (USVs) is critical for autonomous maritime operations in complex environments. However, existing methods often struggle to balance path optimality with computational efficiency under multi-threat constraints. To tackle the challenging path planning problem for USVs in multi-threat maritime environments, this paper presents an adaptive inertia weight and triple learning factors-based competition of tribes and cooperation of members (AIWL-CTCM) algorithm. To enhance the efficiency of static path planning for USVs in complex maritime environments, the proposed AIWL-CTCM algorithm is designed to simultaneously achieving path optimality and navigation safety, thereby balancing multiple key performance criteria including path length, smoothness, collision avoidance, and convergence speed. The algorithm incorporates three key innovations. First, an adaptive inertia weight strategy that iteratively adjusts weights to improve convergence efficiency and solution quality. Second, a sine-acceleration-based optimization of the learning factors, alleviating the limitations imposed by static individual and tribal learning parameters. Third, a dynamic competition factor that varies with fitness values to accelerate the discovery of optimal solutions. Simulation results confirm that the AIWL-CTCM algorithm significantly surpasses the conventional CTCM and other benchmark optimization algorithms in terms of path planning accuracy, convergence speed, and stability.</p>